Optimal Model Selection for Conformalized Robust Optimization Yajie Bao, Yang Hu, Haojie Ren, Peng Zhao and Changliang Zou. arxiv, 2025+.
Conformal Robustness Control: A New Strategy for Robust Decision Yang Hu, Jieren Tan, Changliang Zou, Yajie Bao and Haojie Ren. Submitted, 2025.
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control Yajie Bao, Yuyang Huo, Haojie Ren and Changliang Zou. Journal of Machine Learning Research (JMLR), Accepted, 2025.
Selective Conformal Inference with False Coverage-statement Rate Control Yajie Bao, Yuyang Huo, Haojie Ren and Changliang Zou. Biometrika, 2024.
Semi-Profiled Distributed Estimation for High-Dimensional Partially Linear Model Yajie Bao and Haojie Ren. Computational Statistics & Data Analysis, 2023.
Varying Coefficient Linear Discriminant Analysis for Dynamic Data Yajie Bao and Yuyang Liu. Electronic Journal of Statistics, 2022.
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach Qian Peng, Yajie Bao, Haojie Ren, Zhaojun Wang, Changliang Zou. International Conference on Machine Learning (ICML), 2025.
Error-quantified Conformal Inference for Time Series Junxi Wu, Dongjian Hu, Yajie Bao, Shu-Tao Xia, Changliang Zou. International Conference on Learning Representations (ICLR), 2025.
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective Yajie Bao, Michael Crawshaw and Mingrui Liu. International Conference on Machine Learning (ICML), 2024.
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization Yajie Bao, Amarda Shehu, Mingrui Liu. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds Michael Crawshaw, Yajie Bao and Mingrui Liu. Advances in Neural Information Processing Systems (NeurIPS), 2023.
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data Michael Crawshaw, Yajie Bao and Mingrui Liu. International Conference on Learning Representations (ICLR), 2023.
Fast Composite Optimization and Statistical Recovery in Federated Learning Yajie Bao, Michael Crawshaw, Shan Luo and Mingrui Liu. International Conference on Machine Learning (ICML), 2022.
Byzantine-Tolerant Distributed Multiclass Sparse Linear Discriminant Analysis Yajie Bao, Weidong Liu, Xiaojun Mao and Weijia Xiong. Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
One-Round Communication Efficient Distributed M-Estimation Yajie Bao and Weijia Xiong. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
Step into High-dimensional Statistics: Sparse Mean Estimation
Linear Regression and M-estimator for Diverging p